1. Field of the Invention
This invention relates to estimating a pose of a “pose object”, and more particularly to augmenting pose estimation with a number of positionable beacons, static and/or mobile, that are configured to determine their three-dimensional (3-D) geospatial coordinates and transmit those coordinates to the pose object.
2. Description of the Related Art
Pose estimation and tracking is critical to applications in precision targeting, augmented reality, and geoinformatics among others. The “pose” of a pose object such as a person, robot, unmanned vehicle, sensor, etc. is typically defined by the object's position information (x,y,z) and orientation (roll, pitch, yaw), which together define a six-dimensional (6-D) pose. In some applications, a 3-D pose consisting of only the orientation is sufficient. It is important that the pose estimate be accurate, timely, trackable over a very wide area and robust in changing environmental conditions.
Under the proper conditions, a Global Position System (GPS) can provide accurate position information and can be used to determine orientation. Position information is obtained with a GPS receiver on the pose object in communication with three or more overhead satellites. For example, heading can be determined for moving platforms, and for large moving platforms, such as large aircraft, yaw and roll can be determined (noisily) if GPS receivers are mounted on the wingtips. However, GPS is often attenuated or blocked due to environmental effects. The pose estimate can be inaccurate and experience additional latency when tracking the estimate. Such GPS based systems are thus not sufficient to meet the demands of most applications.
Numerous systems exist for determining the pose of an object (person) relative to a pre-defined coordinate system. Fixed markers or fiducials, typically hundreds of them, are placed at known 3-D locations, typically in an indoor environment. The person wears a visual-based tracking system that images at least three markers simultaneously to recover 2-D image coordinates for the markers. The tracking system correlates the 2-D image coordinates with the known 3-D locations stored in memory to recover the 6-D pose. In practice, this technique can be unstable unless large numbers of markers are visible (6 or more), which for a narrow field-of-view (FOV) camera means the density of markers in the environment must be very large. These solutions tend to be more robust if the markers are spread widely apart (requiring multiple imagers or a wide field-of-view, but the wide field-of-view spreads the resolution across a large area). Furthermore, if the viewpoint changes rapidly the system can become disoriented and take a long time to reacquire the markers and recover. The first use of markers to correct pose information for Augmented Reality was Bajura, Mike and Ulrich Neumann. Dynamic Registration Correction in Augmented-Reality Systems. Proceedings of IEEE VRAIS '95 (Research Triangle Park, N.C., 11-15 Mar. 1995), 189-196. Other examples of these types of systems are described by: Neumann, Ulrich and Youngkwan Cho. A Self-Tracking Augmented Reality System. Proceedings of VRST '96 (Hong Kong, 1-4 Jul. 1996), 109-115 and Welch et al. “High-Performance Wide-Area Optical Tracking: The Hiball Tracking System” Presence: Teleoperators and Virtual Environments vol. 10, #1 (2001), pp. 1-12. Although useful for known indoor environments, these systems are not useful for applications in which the environment is large or changing.
Another approach is to provide the pose object with a GPS receiver that provides position and an inertial sensor package that provides orientation to first estimate and then track the 6-D pose. However, inertial sensors can provide accurate information only for short periods of time and thus the pose estimate tends to drift. The accuracy of such systems has not been demonstrated to be sufficient for typical applications.
More recently, systems are implementing a hybrid approach using inertial sensors and visualization of fixed markers placed at known 3-D locations. The inertial sensors provide an estimate of the pose. The known 3-D locations are correlated to the 2-D image coordinates to make small corrections to the pose estimate. Such systems should be more robust and have less latency. Bajura was also the first to suggest this hybrid approach, using a magnetic-based system to form the initial pose. Other examples are described by State, Andrei, Gentaro Hirota, David T. Chen, Bill Garrett, and Mark Livingston. “Superior Augmented Reality Registration by Integrating Landmark Tracking and Magnetic Tracking,” Proceedings of SIGGRAPH '96 (New Orleans, La., 4-9 Aug. 1996), 429-438; Satoh et al. “A Hybrid Registration Method for Outdoor Augmented Reality”, Proc. ISAR 2001 (New York City, 29-30 Oct. 2001) pp 67-76; Naimark et al. “Circular Data Matrix Fiducial System and Robust Image Processing for a Wearable Vision-Inertial Self-Tracker” Proc. ISMAR 2002 (Darmstadt, Germany, 30 Sep.-1 Oct. 2002) pp. 27-36; and Jiang et al “A Robust Hybrid Tracking System for Outdoor Augmented Reality” Proc. IEEE Virtual Reality 2004 (Chicago, 27-31 Mar., 2004) pp. 3-10. These hybrid systems are an improvement over the vision-based or inertial-base only systems. However, these systems depend upon having fixed markers or landmarks at known 3-D coordinates within the FOV of the object over a particular environment. In many applications it is not practicable to populate the environment with enough markers to ensure visualization at sufficiently high revisit rates to accurately track the pose. Furthermore, it is not practical to pre-position the markers with the desired geometry with respect to an object of interest to obtain the most accurate pose estimate.
There remains a need for a practical pose-estimate system that can provide accurate and timely pose estimates over a very wide area in changing environmental conditions.